A closer look at MOMDPs

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn 2, Issue , 2010, Pages 197-204

  Araya López, Mauricio ^a   Thomas, Vincent ^a   Buffet, Olivier ^b   Charpillet, François ^b  

^a UNIVERSITÉ DE LORRAINE   (France)

^b LORIA   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSICAL ALGORITHMS; COMPLEMENTARY ANALYSIS; DECISION-MAKING PROBLEM; EMPIRICAL EVALUATIONS; LARGE SIZES; PARTIALLY OBSERVABLE MARKOV DECISION PROCESS; STATE SPACE; STATE VARIABLES; VALUE FUNCTIONS; VALUE ITERATION;

MARKOV PROCESSES;

ARTIFICIAL INTELLIGENCE;

EID: 78751476640     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2010.101     Document Type: Conference Paper

References (20)

1. D. Bertsekas and J. Tsitsiklis, Neurodynamic Programming. Athena Scientific, 1996.
2. R. Smallwood and E. Sondik, "The optimal control of partially observable Markov decision processes over a finite horizon," Operation Research, vol. 21, pp. 1071-1088, 1973.
3. A. Cassandra, "A survey of POMDP applications," in AAAI Fall Symposium, 1998.
4. -, "Exact and approximate algorithms for partially observable Markov decision processes," Ph.D. dissertation, Providence, RI, USA, 1998.
5. E. Hansen and Z. Feng, "Dynamic programming for POMDPs using a factored state representation," in Proc. of the Int. Conf. on AI Planning and Scheduling, 2000.
6. S. Ong, S. Png, D. Hsu, and W. Lee, "POMDPs for robotic tasks with mixed observability," in Proceedings of Robotics: Science and Systems V (RSS'09), 2009.
7. W. Zhang and N. L. Zhang, "Restricted value iteration: Theory and algorithms," Journal of Artificial Intelligence Research, vol. 23, pp. 123-165, 2005.
8. D. Hsu, W. Lee, and N. Rong, "What makes some POMDP problems easy to approximate?" in Advances in Neural Processing Systems 21, 2007.
9. A. Dutech and B. Scherrer, "Partially observable Markov decision processes," in Markov Decision Processes in Artificial Intelligence. ISTE / John Wiley, 2010.
10. R. Bellman, "The theory of dynamic programming," Bull. Amer. Math. Soc., vol. 60, pp. 503-516, 1954.
11. G. Monahan, "A survey of partially observable Markov decision processes," Management Science, vol. 28, pp. 1-16, 1982.
12. J. T. C. Papadimitriou, "The complexity of Markov decision processes," Mathematics of Operations Research, vol. 12, no. 3, pp. 441-450, 1987.
13. J. Pineau, G. Gordon, and S. Thrun, "Point-based value iteration: An anytime algorithm for POMDPs," in Proc. of the 18th Int. Joint Conf. on Artificial Intelligence, 2003.
14. T. Smith and R. Simmons, "Heuristic search value iteration for POMDPs," in Proc. of the Int. Conf. on Uncertainty in AI, 2004.
15. M. Spaan and N. Vlassis, "Perseus: Randomized point-based value iteration for POMDPs," Journal of Artificial Intelligence Research, vol. 24, pp. 195-220, 2005.
16. H. Kurniawati, D. Hsu, and W. Lee, "SARSOP: Efficient point-based POMDP planning by approximating optimally reachable belief spaces," in Robotics: Science and Systems IV, 2008.
17. R. Howard and J. Matheson, Readings on the Principles and Applications of Decision Analysis, 1984, vol. II, ch. Influence diagrams (1981).
18. A. Cassandra, M. Littman, and N. Zhang, "Incremental pruning: A simple, fast, exact method for partially observable Markov decision processes," in Proc. of the 13th Conf. on Uncertainty in AI, 1997.
19. R. Zhou and E. Hansen, "An improved grid-based approximation algorithm for POMDPs," in Proc. of the 17th Int. Joint Conf. on Artificial Intelligence, 2001.
20. S. Ross, J. Pineau, S. Paquet, and B. Chaib-draa, "Online planning algorithms for POMDPs," Journal of Artificial Intelligence Research (JAIR), vol. 32, pp. 663-704, 2008.